Deep Learning Resurrects Neural Networks

PORTLAND, Ore. -- Over 2,200 users -- from Fortune 500 companies to hobbyists -- have signed up for Ersatz Labs Inc. (San Francisco, Calif.) free deep-learning beta-software service in the clouds over the last year. But now the cat is out of the bag -- customers can purchase "Ersatz" either as a cloud-based service or an in-house appliance.

What does it do? Picture the computer in Star Trek: You ask the computer to search calculate some problem; it examines your data and answers.

Ersatz combines the latest brain-like neural network algorithms to search and sift through your big data to come up with hard-to-identify trends that you can turn into actionable intelligence -- at least that's the pitch you get from Ersatz Labs chief executive officer (CEO), who credits the deep-learning breakthrough to the researchers in the field whose latest theories are on what Ersatz is based.

"In 2006 a series of algorithmic improvements were made to the multi-layer perceptron [invented in 1957 by Frank Rosenblatt]," Dave Sullivan, co-founder and CEO of Ersatz Labs, told EE Times. "What they discovered was that unsupervised algorithms starting with a random neural network could discover better starting locations, which could then be used to restart the algorithm at those locations, resulting in dramatically better results than when starting with random values. The second big breakthrough came around 2009 when it was discovered that graphics processing units (GPUs) could be configured to speed up neural networks as much as 40 times -- and shortly thereafter that's when Google, Microsoft, Facebook, and the other big boys got involved in a big way."

Ersatz was formed to benefit those companies that want the machine learning abilities of Google, Microsoft, and Facebook on their own datasets, and that don't have the funds or desire to hire a staff of deep learning experts.

The best thing about Ersatz's deep learning neural networks is that they simplify these complex tasks with an easy-to-use web interface and application programmers interface (API). Also, Ersatz does not have to be customized to each application; you just decide on the task you want it to perform -- such as predicting sales or identifying faces in a crowd -- collect historical data on that subject and just throw it at the first layer of Ersatz. It learns raw features from that data, which it passes on to the next layer to learn new, more refined features from those lower-level features, and the process continues. Most problems can be solved with one or two layers, according to Sullivan, but some need more; however, diminishing returns start occurring after about the fourth layer, Sullivan told EE Times. After training is over, the user can just ask Ersatz questions, such as what will next quarter's sales be, and Ersatz provides an intelligent answer. The process works on any storehouse of big data, but Sullivan has spotted two that could start benefiting immediately.

Add Erzatz deep learning runs on its own servers as a cloud service (notice graphics processors at bottom).
(Source: Ersatz)

"Two of our most promising markets is financial services, which we are already seeing a lot of traction to begin with, [and] the other is medical imaging, which deep learning is perfect for because it's unquestionably the state-of-the-art method for spotting abnormalities in medical images," Sullivan said. "At the same time, we are trying to make Ersatz as general purpose as possible from the platform perspective."

For the future, Sullivan promises to extend their software offerings into other branches of machine learning besides deep learning. "People ask us, are we a deep learning startup -- and I say 'no, we are a machine learning startup,' it's just that deep learning seems to be doing very well right now, but that doesn't mean there aren't many more machine learning applications to explore down the road."

Deep Learning just means you are using an artificial neural network--or similar algorithm--to discover relationships among Big Data that would not be obvious to the causual observer. Like Newton's discovery of gravity, many of these discoveries made by Deep Learning algorithms are obvious after they are understood, but difficult to come up with by humans just studying a vast database.